Multi-view　stereo　(MVS)　can　fulfill　dense　three-dimensional　reconstruction　from　a　collection　of　multi-view　images.　Although　deep　learning-based　MVS　has　significantly　enhanced　the　reconstruction　performance,　the　reconstruction　accuracy　and　completeness　still　require　improvements　to　meet　the　need　of　real　applications　of　three-dimensional　content　generation.　So,　a　new　MVS　method,　the　curvature-guided　multi-view　stereo　with　transformers,　is　presented.　By　exploring　inter-view　relationships　and　measuring　the　size　of　the　receptive　field　and　feature　information　on　the　image　surface　using　the　surface　curvature,　the　proposed　method　adapts　to　various　candidate　scales　of　curvatures　to　extract　more　detailed　features　adaptively　for　precise　cost　computation.　Furthermore,　a　transformer-based　feature-matching　network　is　proposed　to　identify　inter-view　similarity　better　and　enhance　feature-matching　accuracy.　Additionally,　a　similarity　measurement　module　based　on　feature　matching　integrates　curvature　and　inter-view　similarity　measurement　tightly　to　further　improve　reconstruction　accuracy.　Experiments　on　the　DTU　dataset　and　Tanks　and　Temples　dataset　validate　the　proposed　method.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2024.